The present invention is generally directed to systems and methods of non-intrusive appliance load monitoring (“NIALM”). Specifically, the present invention is directed to demand side management for residential and small commercial electric energy usage, utilizing software analytics on whole house load profile data to disaggregate into individual appliances and loads.
Appliance load monitoring is an effective way to communicate to users the amount of energy usage required by various appliances. Presenting users with such information in an understandable format allows users to take appropriate action to actively reduce total energy consumption. Moreover, providing itemized information per specific appliance also permits users to determine if acquiring a new or replacement appliance (for example, through purchase, lease, or rental) would reduce energy costs sufficient to validate the price of purchase, lease, or rental. NIALM enables the breakdown of electricity usage for a property without entering the property or applying any sub-metering devices on the individual appliances/devices/loads inside the property. In general, NIALM is known in the prior art. For example, U.S. Pat. No. 4,858,141 to Hart et al. (“Hart”) discusses basic techniques for performing NIALM. Hart teaches generating and using appliance load signatures to extract information for individual loads from whole property load profile data measured by the utility meter. As taught by Hart, information extracted from the utility meter may comprise: power consumption; times when the appliance/load was turned on and off; and appliance/load health.
There has been research in the area of NIALM and various papers have been published on techniques used to define load signatures and run pattern recognition algorithm on the load profile of the property under inspection. Typically, a software analysis is performed on past data collected Therefore such prior art techniques may be useful in breaking down the energy usage or itemizing the electric energy bill post-consumption, but fail to provide near real-time information that may immediately empower users to modify their energy usage. With regard to appliances such as heating or air conditioning—for which usage is based upon immediate conditions—such data of previous usage may provide limited assistance in modifying present behavior and usage.
While the prior art may teach various devices for monitoring and determining power usage (for example, U.S. Patent Application Publication No. 2009/0045804 to Durling et al. (“Durling”)), such devices generally require additional components to be installed or specific devices to be utilized. In addition, prior art techniques and devices have drawbacks in that such devices and techniques generally provide a relatively low confidence level of identifying specific appliances. Such techniques and devices typically do not utilize the most knowledgeable party—the user himself—and also generally fail to account for additional non-electrical information that may be available.
Moreover, prior art techniques and methodologies may provide users with some basic information regarding their power consumption—but fail to provide the user with any additional advice or counseling as to how to effectively use the information to reduce energy consumption. Rather, the user is left with the notion that he or she should simply use particular appliances less often. This information is relatively meaningless with regard to appliances that users generally must use—for example, refrigerators, electric ranges, washing machines, dryers, etc. In addition, with regard to economic efficiency, the time of energy usage may dictate the cost of such usage. For example, during peak energy usage times, utility companies may charge increased rates than during low usage times. Merely changing the time of day a particular appliance is used may result in significant cost savings.
Accordingly, it is desirable to provide systems and methods that may educate users of the various ways in which energy usage—and the resulting costs—may be reduced. It is also desirable that such systems and methods utilize non-electric data (such as weather data, time of day data, neighbor usage, users skill, user education, social priorities, house specific information like size, etc.).